Logistics / Supply ChainNewVerified Real deployment at a named or precisely described company, with a measured result from a primary source.

AI-built load planning tool fills trailers better and cuts outbound freight 12% at a trailer maker

Added 2026-10-16 · Last updated 2026-10-16

Role

Logistics & transportation (outbound load planning) · Typical users: load planner, shipping supervisor, logistics manager, plant manager

The problem

In many plants, outbound loads are still planned by hand. The planner knows which products stack together, what fits on which trailer and which orders can ride together, but that knowledge lives in their head and in a handful of Excel workbooks that do not talk to each other. Trailers leave partly empty, loads have to be locked weeks ahead so the planner can keep up, and any late change in orders is hard to absorb. At a mid-market trailer manufacturer owned by a private-equity firm, outbound freight costs were squeezing margins, load planning was entirely manual and relied on the planner's experience and institutional knowledge, and trailer utilization was sub-optimal.

How AI helps

The consultants found the data and logic needed to optimize loads already existed but were referenced by hand. Instead of replacing the planning platform, they used AI together with the company's own planners to quickly build a purpose-built load planning tool that encodes the experts' stacking logic and replaces the fragmented Excel workbooks with one connected tool. The source does not say which AI was used or whether the tool itself uses machine learning or rules; the planner still owns the final load plan.

Step by step

1. Measure the baseline over a few weeks: trailer utilization (floor space or weight used per load), freight cost per unit or per load, number of loads per week, and how many days before shipment loads are locked.
2. Sit with the planner and write down the rules they apply: which products stack on which, weight and height limits, load sequence by delivery stop, customer and carrier constraints, and the exceptions.
3. Gather the data the rules need in one place: product dimensions and weights, order lines with ship dates and destinations, trailer types and limits. Replace the separate workbooks with one source.
4. Use AI to turn the written rules into a specification and a first tool (spreadsheet or small script) that proposes loads; the planner reviews every proposal against their own plan for the same week.
5. Pilot at one site, compare proposed and actual loads, log every case where the tool breaks a rule the planner knows, and fix the rules before going further.
6. Roll out to other sites only once the pilot holds, and keep a weekly report of utilization, freight cost and lock window against the baseline.
7. Decide on a TMS or optimization platform afterwards, with the measured gain and your written rules in hand.

Tools

• Purpose-built load planning tool developed by SSA & Company with AI and the client's planners: used at the trailer manufacturer (tool not named)
• Other tools to evaluate: load-building and cube optimization software (e.g. EasyCargo, Cargo-Planner, PackVol, Magaya Cargo Planner), the load planning module of your TMS or WMS
• Any LLM (ChatGPT, Claude, Copilot) with the prompt below, to write your planners' stacking rules down and specify a spreadsheet or script before buying anything

As reported by the source; most figures are vendor or self-reported and not independently verified.

Human review: cautions

• The figures come from the consultancy's own case study about an unnamed client; measure your own utilization and freight cost before and after.
• The source does not say what the AI does in the tool; treat this as a method (write the expert's rules down, then automate them), not as a product to buy.
• The planner signs off every load: a wrong stacking or weight rule can damage product, overload an axle or break a delivery sequence.
• Check legal weight and dimension limits and securement rules for your trucks and regions; a tool is only as good as the limits you enter.
• Do not let an LLM calculate weights, axle loads or cube; use the spreadsheet or the optimization tool, and spot-check loads by hand.

You are a logistics engineer helping a manufacturing plant turn its load planner's know-how into written, testable rules.

I will paste: (1) my notes from an interview with our load planner [how they build loads, what stacks on what, limits, exceptions]; (2) our product list [product code, length, width, height, weight, stackable yes/no, fragile yes/no]; (3) our trailer types [inside length, width, height, max payload]; (4) five past loads as the planner built them [load ID, products and quantities, destination stops, ship date].

Do this:
1. Write the planner's rules as a numbered rule table: rule, condition, what to do, source (quote the line from my notes). Mark anything I did not state clearly as 'ASK PLANNER'.
2. List rules that conflict with each other or with the product or trailer data, and the questions to ask the planner to settle them.
3. Check each of the five past loads against the rule table and list any rule the planner did not follow, so we can find unwritten rules.
4. Draft a specification for a spreadsheet or small script that proposes loads from open orders using these rules: inputs, outputs, the order in which rules are applied, and what it must flag for the planner instead of deciding.

Do not add up weights or dimensions or calculate utilization yourself: name the columns and the formulas the spreadsheet should use, and I will compute them. Do not build or lock any load.
Use only the data I paste in; if something is missing, write 'not found' or list it as a question instead of guessing. Do not send, order, post or change anything in any system: produce drafts for my review and approval.

Related cases

• AI freight planning: spot-rate quotes in minutes, consolidated loads and carrier scorecards
• Stop paying freight charges you don't owe: AI flags surcharges, detention and billing errors before payment
• AI orchestrates a plant-attached warehouse: more loads shipped straight from the plant, fewer shuttle moves (P&G Lima)
• Agent sets order quantities and inventory from the production plan

All 7 Logistics / Supply Chain use cases →

Sources

• https://ssaandco.com/case-studies/delivered-12-freight-savings-in-8-weeks-using-ai-at-pe-owned-trailer-manufacturer/